#1. install necessary libraries

library(Seurat)
library(dplyr)
library(ggplot2)
library(patchwork)

options(future.globals.maxSize = 3 * 1024^3)  # Set to 3 GiB

#2 Load Control 10x Data

control = Load10X_Spatial(
     "/Users/cougerjaramillo/Desktop/MS/Intersession 2026/Applications/counts_and_images/1-1",
     filename = "filtered_feature_bc_matrix.h5",
     assay = "Spatial",
     slice = "slice1",
     bin.size = NULL,
     filter.matrix = TRUE,
     to.upper = FALSE,
     image = NULL
)
# Set orig.ident of control to 0
control@meta.data$orig.ident <- "0"
SpatialFeaturePlot(control, features = "nCount_Spatial") + theme(legend.position = "right")

control
An object of class Seurat 
36601 features across 3742 samples within 1 assay 
Active assay: Spatial (36601 features, 0 variable features)
 1 layer present: counts
 1 spatial field of view present: slice1
AD = Load10X_Spatial(
     "/Users/cougerjaramillo/Desktop/MS/Intersession 2026/Applications/counts_and_images/2-8",
     filename = "filtered_feature_bc_matrix.h5",
     assay = "Spatial",
     slice = "slice2",
     bin.size = NULL,
     filter.matrix = TRUE,
     to.upper = FALSE,
     image = NULL
 )
# Set orig.ident of AD to 1
AD@meta.data$orig.ident <- "1"
SpatialFeaturePlot(AD, features = "nCount_Spatial") + theme(legend.position = "right")

AD
An object of class Seurat 
36601 features across 3445 samples within 1 assay 
Active assay: Spatial (36601 features, 0 variable features)
 1 layer present: counts
 1 spatial field of view present: slice2

#3. Pre-processing

#control
#Mitochondrial DNA counts
control[["percent.mt"]] <- PercentageFeatureSet(control, pattern = "^MT-")#
#Visualize
VlnPlot(control, features = c("nFeature_Spatial", "nCount_Spatial", "percent.mt"), ncol = 3)

plot1 <- FeatureScatter(control, feature1 = "nCount_Spatial", feature2 = "percent.mt")
plot2 <- FeatureScatter(control, feature1 = "nCount_Spatial", feature2 = "nFeature_Spatial")
plot1

plot2

#Subset for high quality data
control <- subset(control, subset = nFeature_Spatial > 500 & nFeature_Spatial < 6000 & percent.mt < 40)
#Log normalize
control <- NormalizeData(control, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
#Feature Selection
control <- FindVariableFeatures(control, selection.method = "vst", nfeatures = 2000)
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(control), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(control)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1

plot2

#Scale Data
all.genes <- rownames(control)
control <- ScaleData(control, features = all.genes)

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#check final object for QC
control
An object of class Seurat 
36601 features across 3657 samples within 1 assay 
Active assay: Spatial (36601 features, 2000 variable features)
 3 layers present: counts, data, scale.data
 1 spatial field of view present: slice1
#AD
#Mitochondrial DNA counts
AD[["percent.mt"]] <- PercentageFeatureSet(AD, pattern = "^MT-")#
#Visualize
VlnPlot(AD, features = c("nFeature_Spatial", "nCount_Spatial", "percent.mt"), ncol = 3)

plot1 <- FeatureScatter(AD, feature1 = "nCount_Spatial", feature2 = "percent.mt")
plot2 <- FeatureScatter(AD, feature1 = "nCount_Spatial", feature2 = "nFeature_Spatial")
plot1

plot2

#Subset for high quality data
AD <- subset(AD, subset = nFeature_Spatial > 500 & nFeature_Spatial < 6000 & percent.mt < 40)
#Log normalize
AD <- NormalizeData(AD, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
#Feature Selection
AD <- FindVariableFeatures(AD, selection.method = "vst", nfeatures = 2000)
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(AD), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(AD)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1

plot2

#Scale Data
all.genes <- rownames(AD)
AD <- ScaleData(AD, features = all.genes)

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#check final object for QC
AD
An object of class Seurat 
36601 features across 3366 samples within 1 assay 
Active assay: Spatial (36601 features, 2000 variable features)
 3 layers present: counts, data, scale.data
 1 spatial field of view present: slice2

#4. Merge the control & AD

#Merging Slices
brain.merge <- merge(control, AD, project = "brain")
#VariableFeatures(brain.merge) <- c(VariableFeatures(control), VariableFeatures(AD))
#Re-Scale Data
all.genes <- rownames(brain.merge)
brain.merge <- ScaleData(brain.merge, features = all.genes)

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#Visualize Results
plot1 <- VlnPlot(brain.merge, features = "nCount_Spatial", pt.size = 0.1) + NoLegend()
plot2 <- SpatialFeaturePlot(brain.merge, features = "nCount_Spatial") + theme(legend.position = "right")
plot1

plot2

#5 # Dimensionality reduction, clustering, and visualization


#PCA
brain.merge <- RunPCA(brain.merge, assay = "Spatial", verbose = FALSE)
Error: vector memory limit of 16.0 Gb reached, see mem.maxVSize()

#6 Identifying Cortical Layers by Heatmpap of canonical markers

# Define the list of known markers organized by layer
known_markers_list <- list(
    Layer1 = c("SPARC", "MALAT1", "CXCL14", "ADIRF"),
    Layer2 = c("ENC1", "HPCAL1", "CALB2"),
    Layer3 = c("HOPX"),
    Layer4 = c("RORB", "NUAK1"),
    Layer5 = c("TUBA1B", "TMSB10", "SYT1", "STMN2", "MAP1B", "SNAP25", "PCP4", "UCHL1", "CLSTN2", "SNCG", "SMYD2", "RTN1", "STMN1", "RTN3", "NEFL", "SNRPN", "BASP1", "SYN2", "CALM3", "ENO2", "SNCA", "GAP43", "NAPB", "ELAVL4", "FXYD6", "NDRG4", "NRSN1", "RAB3C", "UHMK1", "SNAP91", "ATP6V1B2", "SLC25A22"),
    Layer6 = c("MAP2K1", "DIRAS2", "KRT17"),
    LayerWM = c("MBP", "MOBP", "CLDND1", "BCAS1", "MTURN", "PAQR6", "HIPK2", "DYNC1LI2") 
)
# Combine all markers into a single vector and create a corresponding layer vector
combined_markers <- unlist(known_markers_list)
layer_labels <- rep(names(known_markers_list), times = sapply(known_markers_list, length))
# Ensure combined markers exist in the Seurat object
valid_markers <- combined_markers[combined_markers %in% rownames(brain.merge)]
valid_layer_labels <- layer_labels[combined_markers %in% rownames(brain.merge)]
# Check if we have valid markers before plotting
if (length(valid_markers) > 0) {
    # Create a named vector for y-axis annotations
    names(valid_layer_labels) <- valid_markers
    
    # Generate the heatmap
    heatmap_plot <- DoHeatmap(brain.merge, features = valid_markers) + 
        NoLegend() + 
        scale_y_discrete(labels = valid_layer_labels) +  # Update y-axis labels
        ggtitle("Combined Heatmap of Known Markers by Layer")  # Title for the heatmap
    
    # Display the heatmap
    print(heatmap_plot)
} else {
    cat("No valid markers found for heatmap generation.\n")
}

#7. Subset the Data by Region

#Subset out anatomical regions
## Subset to clusters of interest
WM <- subset(brain.merge, idents = c(3, 7))

# Extract and attach spatial coordinates from image 1
centroids <- WM[["slice1"]]@boundaries$centroids
coords <- setNames(as.data.frame(centroids@coords), c("x", "y"))
rownames(coords) <- centroids@cells
WM$x <- coords[colnames(WM), "x"]
WM$y <- coords[colnames(WM), "y"]

# After subsetting, we renormalize WM
WN <- NormalizeData(WM, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
WM <- RunPCA(WM, verbose = FALSE)
SpatialDimPlot(WM, label = TRUE, label.size = 3)

#8 WM DEGs

# Join Layers
WM <- JoinLayers(WM)

# Set the identity classes based on the 'orig.ident' column
WM <- SetIdent(WM, value = WM$orig.ident)


# Compare cells with orig.ident values of 0 and 1
WM.AD.markers <- FindMarkers(WM, ident.1 = 0, ident.2 = 1)

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# View the results
head(WM.AD.markers)
---
title: "FINAL PROJECT"
output:
  pdf_document: default
  html_document:
    df_print: paged
  html_notebook: default
---

#1. install necessary libraries
```{r}
library(Seurat)
library(dplyr)
library(ggplot2)
library(patchwork)

options(future.globals.maxSize = 3 * 1024^3)  # Set to 3 GiB
```


#2 Load Control 10x Data
```{r}
control = Load10X_Spatial(
     "/Users/cougerjaramillo/Desktop/MS/Intersession 2026/Applications/counts_and_images/1-1",
     filename = "filtered_feature_bc_matrix.h5",
     assay = "Spatial",
     slice = "slice1",
     bin.size = NULL,
     filter.matrix = TRUE,
     to.upper = FALSE,
     image = NULL
)
# Set orig.ident of control to 0
control@meta.data$orig.ident <- "0"
SpatialFeaturePlot(control, features = "nCount_Spatial") + theme(legend.position = "right")
control


AD = Load10X_Spatial(
     "/Users/cougerjaramillo/Desktop/MS/Intersession 2026/Applications/counts_and_images/2-8",
     filename = "filtered_feature_bc_matrix.h5",
     assay = "Spatial",
     slice = "slice2",
     bin.size = NULL,
     filter.matrix = TRUE,
     to.upper = FALSE,
     image = NULL
 )
# Set orig.ident of AD to 1
AD@meta.data$orig.ident <- "1"
SpatialFeaturePlot(AD, features = "nCount_Spatial") + theme(legend.position = "right")
AD
```


#3. Pre-processing
```{r}
#control
#Mitochondrial DNA counts
control[["percent.mt"]] <- PercentageFeatureSet(control, pattern = "^MT-")#
#Visualize
VlnPlot(control, features = c("nFeature_Spatial", "nCount_Spatial", "percent.mt"), ncol = 3)
plot1 <- FeatureScatter(control, feature1 = "nCount_Spatial", feature2 = "percent.mt")
plot2 <- FeatureScatter(control, feature1 = "nCount_Spatial", feature2 = "nFeature_Spatial")
plot1
plot2
#Subset for high quality data
control <- subset(control, subset = nFeature_Spatial > 500 & nFeature_Spatial < 6000 & percent.mt < 40)
#Log normalize
control <- NormalizeData(control, normalization.method = "LogNormalize", scale.factor = 10000)
#Feature Selection
control <- FindVariableFeatures(control, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(control), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(control)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1
plot2
#Scale Data
all.genes <- rownames(control)
control <- ScaleData(control, features = all.genes)
#check final object for QC
control


#AD
#Mitochondrial DNA counts
AD[["percent.mt"]] <- PercentageFeatureSet(AD, pattern = "^MT-")#
#Visualize
VlnPlot(AD, features = c("nFeature_Spatial", "nCount_Spatial", "percent.mt"), ncol = 3)
plot1 <- FeatureScatter(AD, feature1 = "nCount_Spatial", feature2 = "percent.mt")
plot2 <- FeatureScatter(AD, feature1 = "nCount_Spatial", feature2 = "nFeature_Spatial")
plot1
plot2
#Subset for high quality data
AD <- subset(AD, subset = nFeature_Spatial > 500 & nFeature_Spatial < 6000 & percent.mt < 40)
#Log normalize
AD <- NormalizeData(AD, normalization.method = "LogNormalize", scale.factor = 10000)
#Feature Selection
AD <- FindVariableFeatures(AD, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(AD), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(AD)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1
plot2
#Scale Data
all.genes <- rownames(AD)
AD <- ScaleData(AD, features = all.genes)
#check final object for QC
AD
```


#4. Merge the control & AD 
```{r}
#Merging Slices
brain.merge <- merge(control, AD, project = "brain")
#VariableFeatures(brain.merge) <- c(VariableFeatures(control), VariableFeatures(AD))
#Re-Scale Data
all.genes <- rownames(brain.merge)
brain.merge <- ScaleData(brain.merge, features = all.genes)
#Visualize Results
plot1 <- VlnPlot(brain.merge, features = "nCount_Spatial", pt.size = 0.1) + NoLegend()
plot2 <- SpatialFeaturePlot(brain.merge, features = "nCount_Spatial") + theme(legend.position = "right")
plot1
plot2
```


#5 # Dimensionality reduction, clustering, and visualization
```{r}

#PCA
brain.merge <- RunPCA(brain.merge, assay = "Spatial", verbose = FALSE)
VizDimLoadings(brain.merge, dims = 1:2, reduction = "pca")
DimPlot(brain.merge, reduction = "pca") + NoLegend()
ElbowPlot(brain.merge, ndims = 50)
#Clustering
brain.merge <- FindNeighbors(brain.merge, reduction = "pca", dims = 1:30)
brain.merge <- FindClusters(brain.merge, resolution = 1, verbose = FALSE)
brain.merge <- RunUMAP(brain.merge, reduction = "pca", dims = 1:30)
DimPlot(brain.merge, reduction = "umap", group.by = c("ident", "orig.ident"))
SpatialDimPlot(brain.merge, label = TRUE, label.size = 3)
```


#6 Identifying Cortical Layers by Heatmpap of canonical markers
```{r}
# Define the list of known markers organized by layer
known_markers_list <- list(
    Layer1 = c("SPARC", "MALAT1", "CXCL14", "ADIRF"),
    Layer2 = c("ENC1", "HPCAL1", "CALB2"),
    Layer3 = c("HOPX"),
    Layer4 = c("RORB", "NUAK1"),
    Layer5 = c("TUBA1B", "TMSB10", "SYT1", "STMN2", "MAP1B", "SNAP25", "PCP4", "UCHL1", "CLSTN2", "SNCG", "SMYD2", "RTN1", "STMN1", "RTN3", "NEFL", "SNRPN", "BASP1", "SYN2", "CALM3", "ENO2", "SNCA", "GAP43", "NAPB", "ELAVL4", "FXYD6", "NDRG4", "NRSN1", "RAB3C", "UHMK1", "SNAP91", "ATP6V1B2", "SLC25A22"),
    Layer6 = c("MAP2K1", "DIRAS2", "KRT17"),
    LayerWM = c("MBP", "MOBP", "CLDND1", "BCAS1", "MTURN", "PAQR6", "HIPK2", "DYNC1LI2") 
)
# Combine all markers into a single vector and create a corresponding layer vector
combined_markers <- unlist(known_markers_list)
layer_labels <- rep(names(known_markers_list), times = sapply(known_markers_list, length))
# Ensure combined markers exist in the Seurat object
valid_markers <- combined_markers[combined_markers %in% rownames(brain.merge)]
valid_layer_labels <- layer_labels[combined_markers %in% rownames(brain.merge)]
# Check if we have valid markers before plotting
if (length(valid_markers) > 0) {
    # Create a named vector for y-axis annotations
    names(valid_layer_labels) <- valid_markers
    
    # Generate the heatmap
    heatmap_plot <- DoHeatmap(brain.merge, features = valid_markers) + 
        NoLegend() + 
        scale_y_discrete(labels = valid_layer_labels) +  # Update y-axis labels
        ggtitle("Combined Heatmap of Known Markers by Layer")  # Title for the heatmap
    
    # Display the heatmap
    print(heatmap_plot)
} else {
    cat("No valid markers found for heatmap generation.\n")
}
```


#7. Subset the Data by Region
```{r}
#Subset out anatomical regions
## Subset to clusters of interest
WM <- subset(brain.merge, idents = c(3, 7))

# Extract and attach spatial coordinates from image 1
centroids <- WM[["slice1"]]@boundaries$centroids
coords <- setNames(as.data.frame(centroids@coords), c("x", "y"))
rownames(coords) <- centroids@cells
WM$x <- coords[colnames(WM), "x"]
WM$y <- coords[colnames(WM), "y"]

# After subsetting, we renormalize WM
WN <- NormalizeData(WM, normalization.method = "LogNormalize", scale.factor = 10000)
WM <- RunPCA(WM, verbose = FALSE)
SpatialDimPlot(WM, label = TRUE, label.size = 3)
```


#8 WM DEGs
```{r}
# Join Layers
WM <- JoinLayers(WM)

# Set the identity classes based on the 'orig.ident' column
WM <- SetIdent(WM, value = WM$orig.ident)


# Compare cells with orig.ident values of 0 and 1
WM.AD.markers <- FindMarkers(WM, ident.1 = 0, ident.2 = 1)

# View the results
head(WM.AD.markers)
```
